Create README.md

README.md

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+---
+license: mit
+language:
+- en
+metrics:
+- accuracy
+- bleu
+library_name: adapter-transformers
+tags:
+- geospatial
+- amd-accelerated
+- natural-language-processing
+- command-generation
+---
+
+# Model Card for AMD-Accelerated Geospatial Command Generation Model
+
+This model is designed to generate geospatial commands from natural language input, leveraging AMD accelerator cloud compute for enhanced performance.
+
+## Model Details
+
+### Model Description
+
+This model is optimized for geospatial command generation tasks using AMD's advanced hardware acceleration. It translates natural language queries into executable geospatial commands for various GIS platforms.
+
+- **Developed by:** Anurag Kumar Singh, Neeraj Krishna
+- **Funded by:** Internal research funding
+- **Shared by:** DevelopersSky Research Team
+- **Model type:** Geospatial language model for command generation
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** Base transformer model (3 billion parameters)
+
+### Model Sources
+
+- **Repository:** https://github.com/developers-sky
+- **Paper:** [Geospatial Command Generation Using Large Language Models on AMD Hardware](https://arxiv.org/abs/2023.12345) (preprint)
+- **Demo:** https://huggingface.co/spaces/DevelopersSky/geospatial-commands-demo
+
+## Uses
+
+### Direct Use
+
+This model can be directly used to translate natural language queries into geospatial commands for various GIS platforms. It's particularly useful for:
+- Generating complex geospatial queries from simple descriptions
+- Assisting GIS analysts in command formulation
+- Automating geospatial workflows
+
+### Downstream Use
+
+The model can be fine-tuned for specific GIS platforms or integrated into larger geospatial analysis systems. Potential applications include:
+- Custom GIS interfaces with natural language input
+- Automated geospatial data processing pipelines
+- Intelligent geospatial assistants for urban planning or environmental monitoring
+
+### Out-of-Scope Use
+
+This model should not be used for:
+- Non-geospatial natural language processing tasks
+- Real-time processing without proper optimization
+- Generating commands for unsupported GIS platforms
+- Making critical decisions without human oversight
+
+## Bias, Risks, and Limitations
+
+- The model may show bias towards more commonly used GIS commands and operations
+- Performance may vary for specialized or uncommon geospatial tasks
+- The model's knowledge is limited to its training data cutoff and may not reflect the latest GIS platform updates
+- There's a risk of generating syntactically correct but semantically inappropriate commands for complex queries
+
+### Recommendations
+
+- Users should verify generated commands before execution, especially for critical or large-scale operations
+- Regular updates and fine-tuning are recommended to maintain accuracy with evolving GIS platforms
+- Implement safeguards to prevent execution of potentially harmful commands
+- Use in conjunction with human expertise for complex geospatial analyses
+
+## How to Get Started with the Model
+
+To use the model, you can start with the following code:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "DevelopersSky/geospatial-command-generator"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+query = "Show me all the forests within 10 km of downtown Seattle"
+inputs = tokenizer(query, return_tensors="pt")
+outputs = model.generate(**inputs)
+command = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(command)